Journal
2022 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV 2022)
Volume -, Issue -, Pages 1779-1788Publisher
IEEE COMPUTER SOC
DOI: 10.1109/WACV51458.2022.00184
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Compressed sensing is widely used in reducing the acquisition time of magnetic resonance imaging. This study proposes a complex-valued generative adversarial network framework for reconstructing complex-valued MRI data and introduces a novel complex-valued activation function that is sensitive to the input phase.
Compressed sensing (CS) is extensively used to reduce magnetic resonance imaging (MRI) acquisition time. State-of-the-art deep learning-based methods have proven effective in obtaining fast, high-quality reconstruction of CS-MR images. However, they treat the inherently complex-valued MRI data as real-valued entities by extracting the magnitude content or concatenating the complex-valued data as two real-valued channels for processing. In both cases, the phase content is discarded. To address the fundamental problem of real-valued deep networks, i.e. their inability to process complex-valued data, we propose a complex-valued generative adversarial network (Co-VeGAN) framework, which is the first-of-its-kind generative model exploring the use of complex-valued weights and operations. Further, since real-valued activation functions do not generalize well to the complex-valued space, we propose a novel complex-valued activation function that is sensitive to the input phase and has a learnable profile. Extensive evaluation of the proposed approach' on different datasets demonstrates that it significantly outperforms the existing CS-MRI reconstruction techniques.
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